Impact of socioeconomic status on survival in patients with ovarian cancer

Factor Analysis of Health Care Access With Ovarian Cancer Surgery and Gynecologic Oncologist Consultation

IMPORTANCE

Poor health care access (HCA) is associated with racial and ethnic disparities in ovarian cancer (OC) survival.

OBJECTIVE

To generate composite scores representing health care affordability, availability, and accessibility via factor analysis and to evaluate the association between each score and key indicators of guideline-adherent care.

DESIGN, SETTING, AND PARTICIPANTS

This retrospective cohort study used data from patients with OC diagnosed between 2008 and 2015 in the Surveillance, Epidemiology, and End Results (SEER) Medicare database. The SEER Medicare database uses cancer registry data and linked Medicare claims from 12 US states. Included patients were Hispanic, non-Hispanic Black, and non-Hispanic White individuals aged 65 years or older diagnosed from 2008 to 2015 with first or second primary OC of any histologic type (International Classification of Diseases for Oncology, 3rd Edition [ICD-O-3] code C569). Data were analyzed from June 2020 to June 2022.

EXPOSURES

The SEER-Medicare data set was linked with publicly available data sets to obtain 35 variables representing health care affordability, availability, and accessibility. A composite score was created for each dimension using confirmatory factor analysis followed by a promax (oblique) rotation on multiple component variables.

MAIN OUTCOMES AND MEASURES

The main outcomes were consultation with a gynecologic oncologist for OC and receipt of OC-related surgery in the 2 months prior to or 6 months after diagnosis.

RESULTS

The cohort included 8987 patients, with a mean (SD) age of 76.8 (7.3) years and 612 Black patients (6.8%), 553 Hispanic patients (6.2%), and 7822 White patients (87.0%). Black patients (adjusted odds ratio [aOR], 0.75; 95% CI, 0.62-0.91) and Hispanic patients (aOR, 0.81; 95% CI, 0.67-0.99) were less likely to consult a gynecologic oncologist compared with White patients, and Black patients were less likely to receive surgery after adjusting for demographic and clinical characteristics (aOR, 0.76; 95% CI, 0.62-0.94). HCA availability and affordability were each associated with gynecologic oncologist consultation (availability: aOR, 1.16; 95% CI, 1.09-1.24; affordability: aOR, 1.13; 95% CI, 1.07-1.20), while affordability was associated with receipt of OC surgery (aOR, 1.08; 95% CI, 1.01-1.15). In models mutually adjusted for availability, affordability, and accessibility, Black patients remained less likely to consult a gynecologic oncologist (aOR, 0.80; 95% CI, 0.66-0.97) and receive surgery (aOR, 0.80; 95% CI, 0.65-0.99).

CONCLUSIONS AND RELEVANCE

In this cohort study of Hispanic, non-Hispanic Black, and non-Hispanic White patients with OC, HCA affordability and availability were significantly associated with receiving surgery and consulting a gynecologic oncologist. However, these dimensions did not fully explain racial and ethnic disparities.

The socioeconomic gradient in mortality from ovarian, cervical, and endometrial cancer in Australian women, 2001-2018: A population-based study

BACKGROUND

Socio-economic (SE) status is closely linked to health status and the mechanisms of this association are complex. One important adverse effect of SE disadvantage is vulnerability to cancer and cancer is a major cause of morbidity and mortality in Australia.

AIMS

We aimed to estimate the effect of SE status on mortality rates from ovarian, cervical, and endometrial cancer.

MATERIALS AND METHODS

National mortality data were obtained from the Australian Bureau of Statistics (ABS) for the calendar years from 2001 to 2018, inclusive. Individual deaths were grouped by the ABS Index of Relative Socio-economic Advantage and Disadvantage. Population data were obtained to provided denominators allowing calculation of mortality rates (deaths per 100 000 women aged 30-79 years). Statistical analyses performed included tabulating point-estimates of mortality rates and their changes over time and modelling the trends of rates using maximum likelihood method.

RESULTS

Age-standardised mortality rates for ovarian and cervical cancer fell over the study period but increased for endometrial cancer. There was clear evidence of a SE gradient in the mortality rate for all three cancers. This SE gradient increased over the study period for ovarian and cervical cancer but remained unchanged for endometrial cancer.

CONCLUSIONS

Women at greater SE disadvantage have higher rates of death from the commonest gynaecological cancers and this gradient has not reduced over the last two decades. After the COVID-19 pandemic efforts must be redoubled to ensure that Australians already at risk of ill health do not face even greater risks because of their circumstances.

Medicaid expansion and 2-year survival in women with gynecologic cancer: a difference-in-difference analysis

BACKGROUND

The Affordable Care Act implemented optional Medicaid expansion starting in 2014, but the association between Medicaid expansion and gynecologic cancer survival is unknown.

OBJECTIVE

To evaluate the impact of Medicaid expansion by comparing 2-year survival among gynecologic cancers before and after 2014 in states that did and did not expand Medicaid using a difference-in-difference analysis.

STUDY DESIGN

We searched the National Cancer Database for women aged 40 to 64 years, diagnosed with a primary gynecologic malignancy (endometrial, ovarian, cervical, vulvar, and vaginal) between 2010 and 2016. We used a quasiexperimental difference-in-difference multivariable Cox regression analysis to compare 2-year survival between states that expanded Medicaid in January 2014 and states that did not expand Medicaid as of 2016. We performed univariable subgroup difference-in-difference Cox regression analyses on the basis of stage, income, race, ethnicity, and geographic location. Adjusted linear difference-in-difference regressions evaluated the proportion of uninsured patients on the basis of expansion status after 2014. We evaluated adjusted Kaplan-Meier curves to examine differences on the basis of study period and expansion status.

RESULTS

Our sample included 169,731 women, including 78,669 (46.3%) in expansion states and 91,062 (53.7%) in nonexpansion states. There was improved 2-year survival on adjusted difference-in-difference Cox regressions for women with ovarian cancer in expansion than in nonexpansion states after 2014 (hazard ratio, 0.88; 95% confidence interval, 0.82-0.94; P<.001) with no differences in endometrial, cervical, vaginal, vulvar, or combined gynecologic cancer sites on the basis of expansion status. On univariable subgroup difference-in-difference Cox analyses, women with ovarian cancer with stage III-IV disease (P=.008), non-Hispanic ethnicity (P=.042), those in the South (P=.016), and women with vulvar cancer in the Northeast (P=.022), had improved 2-year survival in expansion than in nonexpansion states after 2014. In contrast, women with cervical cancer in the South (P=.018) had worse 2-year survival in expansion than in nonexpansion states after 2014. All cancer sites had lower proportions of uninsured patients in expansion than in nonexpansion states after 2014.

CONCLUSION

There was a significant association between Medicaid expansion and improved 2-year survival for women with ovarian cancer in states that expanded Medicaid after 2014. Despite improved insurance coverage, racial, ethnic, and regional survival differences exist between expansion and nonexpansion states.

How do age and social environment affect the dynamics of death hazard and survival in patients with breast or gynecological cancer in France?

Several studies have investigated the association between net survival (NS) and social inequalities in people with cancer, highlighting a varying influence of deprivation depending on the type of cancer studied. However, few of these studies have accounted for the effect of social inequalities over the follow-up period, and/or according to the age of the patients. Thus, using recent and more relevant statistical models, we investigated the effect of social environment on NS in women with breast or gynecological cancer in France. The data were derived from population-based cancer registries, and women diagnosed with breast or gynecological cancer between 2006 and 2009 were included. We used the European deprivation index (EDI), an aggregated index, to define the social environment of the women included. Multidimensional penalized splines were used to model excess mortality hazard. We observed a significant effect of the EDI on NS in women with breast cancer throughout the follow-up period, and especially at 1.5 years of follow-up in women with cervical cancer. Regarding corpus uteri and ovarian cancer patients, the effect of deprivation on NS was less pronounced. These results highlight the impact of social environment on NS in women with breast or gynecological cancer in France thanks to a relevant statistical approach, and identify the follow-up periods during which the social environment may have a particular influence. These findings could help investigate targeted actions for each cancer type, particularly in the most deprived areas, at the time of diagnosis and during follow-up.

Increasing Area Deprivation Index negatively impacts ovarian cancer survival

INTRODUCTION

While individual-level measures of socioeconomic status have been well-studied in relation to ovarian cancer survival, no studies to date have examined both state and national-level Area Deprivation Indices (ADIs), which incorporate neighborhood affluence and resources.

METHODS

We abstracted clinical data from medical records for ovarian cancer cases from the Vanderbilt University Medical Center and obtained ADIs from the Neighborhood Atlas®. Associations with clinical characteristics were assessed with Spearman correlations and Kruskal-Wallis tests; associations with progression-free survival (PFS) and overall survival (OS) were assessed with Cox proportional-hazards regression.

RESULTS

Among 184 cases, state and national ADIs were highly correlated, but not related to any cancer characteristics. In multivariable adjusted regression models, both were significantly associated with OS; each decile increase in state or national ADI corresponded to a 9 % or 10 % greater risk of death, respectively.

CONCLUSIONS

Increasing area-level deprivation may negatively impact ovarian cancer survival.

Factors Explaining Socio-Economic Inequalities in Cancer Survival: A Systematic Review

BACKGROUND

There is strong and well-documented evidence that socio-economic inequality in cancer survival exists within and between countries, but the underlying causes of these differences are not well understood.

METHODS

We systematically searched the Ovid Medline, EMBASE, and CINAHL databases up to 31 May 2020. Observational studies exploring pathways by which socio-economic position (SEP) might causally influence cancer survival were included.

RESULTS

We found 74 eligible articles published between 2005 and 2020. Cancer stage, other tumor characteristics, health-related lifestyle behaviors, co-morbidities and treatment were reported as key contributing factors, although the potential mediating effect of these factors varied across cancer sites. For common cancers such as breast and prostate cancer, stage of disease was generally cited as the primary explanatory factor, while co-morbid conditions and treatment were also reported to contribute to lower survival for more disadvantaged cases. In contrast, for colorectal cancer, most studies found that stage did not explain the observed differences in survival by SEP. For lung cancer, inequalities in survival appear to be partly explained by receipt of treatment and co-morbidities.

CONCLUSIONS

Most studies compared regression models with and without adjusting for potential mediators; this method has several limitations in the presence of multiple mediators that could result in biased estimates of mediating effects and invalid conclusions. It is therefore essential that future studies apply modern methods of causal mediation analysis to accurately estimate the contribution of potential explanatory factors for these inequalities, which may translate into effective interventions to improve survival for disadvantaged cancer patients.

Social Inequalities in Cancer Survival in Belgium: A Population-Based Cohort Study

BACKGROUND

Socioeconomic status (SES) is an important factor in cancer survival; however, results are heterogeneous and linked to characteristics of the study population and health care system. This population-based cohort study evaluates the association between individual-level socioeconomic and demographic factors and cancer survival for the first time in Belgium.

METHODS

From the Belgian Cancer Registry, we identified 109,591 patients diagnosed between 2006 and 2013 with one of eight common cancer types. Information on treatment, socioeconomic parameters, and vital status were retrieved from multiple data sources and linked using a unique personal identification number. The outcome was 5-year observed survival. Associations between survival and socioeconomic and demographic factors were assessed using multivariable Cox proportional-hazard regression models.

RESULTS

Lower income, unemployment, and living alone were all associated with worse cancer survival. These associations were most pronounced for certain lifestyle-related cancer types (e.g., head and neck cancers) and those with good to moderate prognosis (e.g., colorectal and female breast cancer).

CONCLUSIONS

These results indicate that, despite a comprehensive and nationwide health insurance program in which equity in rights and access to health care are pursued, SES is associated with disparities in cancer survival in Belgium.

IMPACT

This population-based study with individual-level socioeconomic information of more than 100,000 patients with cancer identifies patient groups that may be at highest risk for socioeconomic disparities in cancer survival. Reasons behind the observed disparities are multiple and complex and should be further examined. Health policy interventions should consider the observed deprivation gap to plan targeted actions.